AI Engineer

• Implement new AI features in products
• Build tools & frameworks augmenting the capabilities of LLM/VLMs
• Build AI automations & AI agents
• Fine-tune AI models
• Fine-tune and maintain models with the HuggingFace toolkit
• Monitor model's performance
• Work with and guide internal teams on building custom datasets
• Rapidly build and test AI-driven concepts — moving from proof of concept → MVP → production.
• Evaluate existing solutions (both internal & external) and conduct experiments on potential improvements

• Build autonomous agents using frameworks like LangChain, LangGraph, AutoGen, and CrewAI that can plan, reason, and execute tasks iteratively.
• Integrate and fine-tune LLMs (e.g., GPT, Claude, LLaMA) for use in agent workflows, including prompt engineering and retrieval-augmented generation (RAG).
• Develop systems where multiple agents collaborate, delegate tasks, and adapt strategies based on real-time feedback.
• Enable agents to interact with external systems (e.g., APIs, databases, web apps) to perform actions autonomously.
• Use tools like LangSmith, Grafana, and Splunk to monitor agent behavior, performance, and reliability.
• Work closely with data scientists, architects, and product teams to align agentic solutions with business goals.
• Ensure all solutions are well-documented and comply with data privacy and security standards.

Agentic AI Developer

• Lead and oversee the AI team in designing, developing, and deploying AI and machine learning models and solutions.
• Define and execute the AI strategy aligned with business goals and emerging industry trends.
• Collaborate with cross-functional teams including data science, engineering, product management, and business units to identify AI-driven opportunities and deliver impactful solutions.
• Manage AI projects ensuring timely delivery, quality, and alignment with organizational objectives.
• Mentor and develop the AI team by fostering skills growth, collaboration, and innovative thinking.
• Stay abreast of the latest AI research, tools, and methodologies and assess their applicability to business problems.
• Communicate complex AI concepts clearly to non-technical stakeholders to facilitate informed decision-making.
• Advocate best practices for AI ethics, data privacy, and security within the organization.

AI Lead

• Design, develop, and implement artificial intelligence solutions.
• Work closely with data scientists, engineers, and stakeholders.
• Build scalable AI models and integrate them into product offerings.
• Involved in all stages of AI project development from data collection to model monitoring.

• Design, implement, and optimize Agentic AI solutions
• Collaborate with cross-functional teams to enhance operations
• Maintain and evaluate AI performance and troubleshoot workflow issues
• Train and deploy generative AI models
• Build and maintain AI-powered automations and integrations

Machine Learning Engineer

• Design, build, and maintain scalable ML pipelines for training, testing, and deployment
• Deploy & maintain machine learning models and ensure their performance, reliability, and monitoring
• Collaborate with data scientists and engineers to streamline experimentation and deployment workflows
• Implement CI/CD practices for ML systems (ML CI/CD)
• Manage and optimize cloud-based infrastructure for ML workloads
• Develop monitoring, logging, and alerting systems for model performance and data drift
• Ensure reproducibility, versioning, and governance of ML models and datasets
• Advocate for best practices in MLOps, DevOps, and software engineering

Senior MLOps Engineer

• Build and Maintain MLOps Pipelines: Design and implement robust CI/CD pipelines for training, evaluating, and deploying machine learning models.
• Manage Cloud Infrastructure: Provision, configure, and manage our cloud infrastructure (AWS, GCP, or Azure) using Infrastructure as Code (IaC) principles.
• Automate Everything: Automate the deployment, monitoring, and scaling of machine learning services and data pipelines.
• Containerization & Orchestration: Utilize Docker and Kubernetes to create and manage reproducible environments for our ML applications.
• Collaborate and Empower: Work closely with AI engineers and data scientists to understand their needs and create tools that streamline their workflows.
• Ensure System Health: Implement comprehensive monitoring and logging to ensure the high availability, performance, and security of our ML platform.

• Support data collection, cleaning, and preprocessing for ML and LLM projects.
• Assist in implementing, evaluating, and optimizing AI/ML models and LLM pipelines.
• Develop and test prompt templates, retrieval chains, and agentic components under guidance.
• Contribute to fine-tuning and lightweight adaptation (LoRA, adapters, instruction-tuning) of models.
• Implement model APIs or inference endpoints, and integrate them into applications or microservices.
• Participate in debugging, troubleshooting, and monitoring both ML pipelines and agentic workflows.
• Document data flows, model architectures, and experiment results clearly.
• Stay current with advances in machine learning, generative AI, and agent frameworks (LangChain, LangGraph, vLLM etc.).

AI Engineer I

• Design, develop, and deploy AI agents capable of performing autonomous tasks.
• Build and fine-tune solutions using LLMs (e.g., OpenAI, Gemini, Claude, Mistral, LLaMA).
• Proficient in Retrieval-Augmented Generation (RAG) systems and experienced with vector databases, leveraging these technologies to enhance AI model performance and scalability.
• Implement communication between agents using agent orchestration frameworks.
• Automate workflows for sales, project management, accounting, and localization.
• Collaborate with backend and frontend teams to integrate AI services.
• Conduct prompt engineering and evaluation of model outputs.
• Ensure the scalability and performance of AI systems in production environments.

• Design, develop, and deploy AI agents capable of performing autonomous tasks.
• Build and fine-tune solutions using LLMs (e.g., OpenAI, Gemini, Claude, Mistral, LLaMA).
• Proficient in Retrieval-Augmented Generation (RAG) systems and experienced with vector databases, leveraging these technologies to enhance AI model performance and scalability
• Implement communication between agents using agent orchestration frameworks.
• Automate workflows for sales, project management, accounting, and localization.
• Collaborate with backend and frontend teams to integrate AI services.
• Conduct prompt engineering and evaluation of model outputs.
• Ensure the scalability and performance of AI systems in production environments.

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AI Engineer I

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